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Model Run

A Model Run represents executions of a Model with particular configurations and specific outputs. In general, it is best to start a new Model Run whenever significant changes have been made to the code version or assumptions to preserve history within the graph. However, Model Runs are flexible and users can decide how they want to organize their work. For example, users can create Model Runs associated with each handoff dataset or a single Model Run for all handoff datasets.

There are three main aspects that are specified when initializing a Model Run:

  1. Model code and configurations
  2. Handoff datasets
  3. Handoff tasks

The Model Run Config outlines all expected transformations for each handoff and PIPES uses these details to track progress in Level 2 (operations) to ensure that all expected handoff transformations have been completed. See Validation for more explanation on how requirements differences are determined and examples.

In addition to transformations, QAQC and visualization Tasks can also be added to model runs to account for expected validation steps made during this step in the pipeline. Completed work will be validated against these in future parts of the workflow. See Progress Tracking to learn more.

For more specifics on the metadata keys and their types in the Model Run template, see the Model Run Config.

When you are finished with work on a Model Run, or if you simply just want to end the Model Run work early, you can close it. See Closing a Model Run for more information.

For schema information, please see the Model Run Config.